Overview

Dataset statistics

Number of variables20
Number of observations295499
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 9 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 8 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with R1 (MOhm) and 11 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 10 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 3 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -107.4011981)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32137 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:32:52.052200
Analysis finished2022-12-20 08:34:50.346102
Duration1 minute and 58.29 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45450.373
Minimum0
Maximum90909.668
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:51.904015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4538.9471
Q122714.702
median45463.664
Q368177.09
95-th percentile86363.742
Maximum90909.668
Range90909.668
Interquartile range (IQR)45462.388

Descriptive statistics

Standard deviation26247.248
Coefficient of variation (CV)0.57749247
Kurtosis-1.2003865
Mean45450.373
Median Absolute Deviation (MAD)22731.202
Skewness-0.0002246206
Sum1.343054 × 1010
Variance6.8891805 × 108
MonotonicityStrictly increasing
2022-12-20T14:04:52.130457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60636.364 1
 
< 0.1%
60607.94 1
 
< 0.1%
60607.631 1
 
< 0.1%
60607.322 1
 
< 0.1%
60607.013 1
 
< 0.1%
60606.706 1
 
< 0.1%
60606.396 1
 
< 0.1%
60606.087 1
 
< 0.1%
60605.778 1
 
< 0.1%
Other values (295489) 295489
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.308 1
< 0.1%
0.616 1
< 0.1%
0.926 1
< 0.1%
1.234 1
< 0.1%
1.542 1
< 0.1%
1.851 1
< 0.1%
2.157 1
< 0.1%
2.467 1
< 0.1%
2.774 1
< 0.1%
ValueCountFrequency (%)
90909.668 1
< 0.1%
90909.36 1
< 0.1%
90909.051 1
< 0.1%
90908.743 1
< 0.1%
90908.433 1
< 0.1%
90908.123 1
< 0.1%
90907.815 1
< 0.1%
90907.506 1
< 0.1%
90907.198 1
< 0.1%
90906.889 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct302
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9019695
Minimum0
Maximum20
Zeros32137
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:52.282012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.427912
Coefficient of variation (CV)0.6491549
Kurtosis-1.2332437
Mean9.9019695
Median Absolute Deviation (MAD)6.67
Skewness0.0090071337
Sum2926022.1
Variance41.318053
MonotonicityNot monotonic
2022-12-20T14:04:52.443379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32137
10.9%
20 29267
9.9%
8.89 29266
9.9%
2.22 29237
9.9%
13.33 29234
9.9%
6.67 29230
9.9%
17.78 29224
9.9%
15.56 29208
9.9%
11.11 29204
9.9%
4.44 29199
9.9%
Other values (292) 293
 
0.1%
ValueCountFrequency (%)
0 32137
10.9%
0.0266 1
 
< 0.1%
0.0333 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2311 1
 
< 0.1%
0.3596 1
 
< 0.1%
0.4085 1
 
< 0.1%
0.5136 1
 
< 0.1%
0.7002 1
 
< 0.1%
0.7148 1
 
< 0.1%
ValueCountFrequency (%)
20 29267
9.9%
19.9023 1
 
< 0.1%
19.3468 1
 
< 0.1%
19.2444 1
 
< 0.1%
19.2208 1
 
< 0.1%
19.1253 1
 
< 0.1%
18.7746 1
 
< 0.1%
18.6132 1
 
< 0.1%
18.5348 1
 
< 0.1%
18.062 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct23177
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.726298
Minimum16.35
Maximum74.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:52.620489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.35
5-th percentile23.03
Q136.16
median46.55
Q355.69
95-th percentile65.29
Maximum74.65
Range58.3
Interquartile range (IQR)19.53

Descriptive statistics

Standard deviation12.671205
Coefficient of variation (CV)0.27710979
Kurtosis-0.70791258
Mean45.726298
Median Absolute Deviation (MAD)9.79
Skewness-0.11283468
Sum13512075
Variance160.55943
MonotonicityNot monotonic
2022-12-20T14:04:52.763649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.02 5168
 
1.7%
30.68 2638
 
0.9%
46.53 2466
 
0.8%
37.23 2432
 
0.8%
47.56 2165
 
0.7%
36.17 2155
 
0.7%
44.45 1904
 
0.6%
48.58 1892
 
0.6%
48.59 1790
 
0.6%
43.41 1776
 
0.6%
Other values (23167) 271113
91.7%
ValueCountFrequency (%)
16.35 193
0.1%
16.3503 2
 
< 0.1%
16.3505 1
 
< 0.1%
16.3508 1
 
< 0.1%
16.3509 1
 
< 0.1%
16.351 1
 
< 0.1%
16.3513 1
 
< 0.1%
16.3514 1
 
< 0.1%
16.3515 1
 
< 0.1%
16.412 1
 
< 0.1%
ValueCountFrequency (%)
74.65 394
0.1%
74.6499 1
 
< 0.1%
74.6498 1
 
< 0.1%
74.6493 1
 
< 0.1%
74.6492 1
 
< 0.1%
74.6488 1
 
< 0.1%
74.6486 1
 
< 0.1%
74.6483 1
 
< 0.1%
74.6482 1
 
< 0.1%
74.6473 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct6684
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.010985
Minimum25.38
Maximum27.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:52.926085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.38
5-th percentile25.4599
Q125.54
median25.66
Q326.66
95-th percentile26.98
Maximum27.18
Range1.8
Interquartile range (IQR)1.12

Descriptive statistics

Standard deviation0.57935315
Coefficient of variation (CV)0.022273403
Kurtosis-1.1807897
Mean26.010985
Median Absolute Deviation (MAD)0.2
Skewness0.69205923
Sum7686220.2
Variance0.33565007
MonotonicityNot monotonic
2022-12-20T14:04:53.205806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.5 40942
 
13.9%
25.58 27116
 
9.2%
25.54 17372
 
5.9%
25.66 15907
 
5.4%
25.62 15049
 
5.1%
25.42 13138
 
4.4%
25.46 11302
 
3.8%
26.98 9847
 
3.3%
26.86 9047
 
3.1%
25.9 8865
 
3.0%
Other values (6674) 126914
42.9%
ValueCountFrequency (%)
25.38 479
0.2%
25.3801 18
 
< 0.1%
25.3802 1
 
< 0.1%
25.3807 1
 
< 0.1%
25.3808 1
 
< 0.1%
25.3828 1
 
< 0.1%
25.3831 1
 
< 0.1%
25.3832 1
 
< 0.1%
25.3844 1
 
< 0.1%
25.3851 1
 
< 0.1%
ValueCountFrequency (%)
27.18 1550
0.5%
27.1799 25
 
< 0.1%
27.1798 17
 
< 0.1%
27.1792 1
 
< 0.1%
27.1788 1
 
< 0.1%
27.1785 1
 
< 0.1%
27.1781 1
 
< 0.1%
27.178 1
 
< 0.1%
27.1779 1
 
< 0.1%
27.1778 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11377
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94854
Minimum0
Maximum321.2093
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:53.379241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7533
Q1239.8956
median239.9723
Q3240.0455
95-th percentile240.1834
Maximum321.2093
Range321.2093
Interquartile range (IQR)0.1499

Descriptive statistics

Standard deviation1.7930832
Coefficient of variation (CV)0.0074727824
Kurtosis13746.462
Mean239.94854
Median Absolute Deviation (MAD)0.0749
Skewness-107.4012
Sum70904553
Variance3.2151474
MonotonicityNot monotonic
2022-12-20T14:04:53.526327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9517 138
 
< 0.1%
239.9841 135
 
< 0.1%
239.971 134
 
< 0.1%
239.9805 133
 
< 0.1%
240.0057 133
 
< 0.1%
239.9688 133
 
< 0.1%
239.9513 132
 
< 0.1%
239.9867 132
 
< 0.1%
239.9938 132
 
< 0.1%
239.9974 131
 
< 0.1%
Other values (11367) 294166
99.5%
ValueCountFrequency (%)
0 8
< 0.1%
0.0386 1
 
< 0.1%
0.557 1
 
< 0.1%
1.0738 1
 
< 0.1%
1.5939 1
 
< 0.1%
63.3839 1
 
< 0.1%
96.5254 1
 
< 0.1%
98.674 1
 
< 0.1%
136.5259 1
 
< 0.1%
148.4054 1
 
< 0.1%
ValueCountFrequency (%)
321.2093 1
< 0.1%
315.6031 1
< 0.1%
292.5017 1
< 0.1%
287.8195 1
< 0.1%
276.3673 1
< 0.1%
265.5716 1
< 0.1%
264.666 1
< 0.1%
264.2699 1
< 0.1%
263.8868 1
< 0.1%
259.9459 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1753
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35511257
Minimum0.198
Maximum0.9009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:53.691877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.198
5-th percentile0.1995
Q10.2
median0.2
Q30.207
95-th percentile0.899
Maximum0.9009
Range0.7029
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28855101
Coefficient of variation (CV)0.8125621
Kurtosis-0.20468512
Mean0.35511257
Median Absolute Deviation (MAD)0
Skewness1.3375184
Sum104935.41
Variance0.083261685
MonotonicityNot monotonic
2022-12-20T14:04:53.839512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 155866
52.7%
0.899 22604
 
7.6%
0.898 10651
 
3.6%
0.201 7872
 
2.7%
0.199 4318
 
1.5%
0.202 2726
 
0.9%
0.1994 2565
 
0.9%
0.1998 2504
 
0.8%
0.1992 2477
 
0.8%
0.1995 2472
 
0.8%
Other values (1743) 81444
27.6%
ValueCountFrequency (%)
0.198 1
 
< 0.1%
0.1981 2
 
< 0.1%
0.1982 1
 
< 0.1%
0.1983 1
 
< 0.1%
0.1984 5
< 0.1%
0.1985 3
< 0.1%
0.1986 1
 
< 0.1%
0.1987 2
 
< 0.1%
0.1988 2
 
< 0.1%
0.1989 2
 
< 0.1%
ValueCountFrequency (%)
0.9009 1
 
< 0.1%
0.9008 1
 
< 0.1%
0.9007 2
 
< 0.1%
0.9006 1
 
< 0.1%
0.9001 1
 
< 0.1%
0.9 1362
0.5%
0.8999 744
0.3%
0.8998 786
0.3%
0.8997 770
0.3%
0.8996 787
0.3%

R1 (MOhm)
Real number (ℝ)

Distinct8511
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.264653
Minimum0.0328
Maximum128.0195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:53.999599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0328
5-th percentile0.0786
Q10.423
median1.988
Q328.6645
95-th percentile69.1448
Maximum128.0195
Range127.9867
Interquartile range (IQR)28.2415

Descriptive statistics

Standard deviation23.639494
Coefficient of variation (CV)1.4534275
Kurtosis0.85291395
Mean16.264653
Median Absolute Deviation (MAD)1.9061
Skewness1.4180108
Sum4806188.8
Variance558.82569
MonotonicityNot monotonic
2022-12-20T14:04:54.159589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.1111 724
 
0.2%
71.3877 720
 
0.2%
70.7619 688
 
0.2%
72.5638 685
 
0.2%
69.1448 683
 
0.2%
69.6423 679
 
0.2%
74.3444 676
 
0.2%
70.2486 666
 
0.2%
0.0954 661
 
0.2%
71.9176 658
 
0.2%
Other values (8501) 288659
97.7%
ValueCountFrequency (%)
0.0328 1
 
< 0.1%
0.0329 1
 
< 0.1%
0.0334 1
 
< 0.1%
0.0335 1
 
< 0.1%
0.0338 3
< 0.1%
0.034 2
< 0.1%
0.0341 1
 
< 0.1%
0.0345 1
 
< 0.1%
0.0346 1
 
< 0.1%
0.0347 3
< 0.1%
ValueCountFrequency (%)
128.0195 1
 
< 0.1%
114.818 2
 
< 0.1%
113.4868 1
 
< 0.1%
111.9292 10
 
< 0.1%
110.6632 15
 
< 0.1%
109.181 23
< 0.1%
107.9756 35
< 0.1%
106.5634 34
< 0.1%
105.4143 39
< 0.1%
104.0673 49
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8233
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.333838
Minimum0.0559
Maximum199.8105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:54.324773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0559
5-th percentile0.1402
Q10.4963
median1.5146
Q332.2705
95-th percentile78.3034
Maximum199.8105
Range199.7546
Interquartile range (IQR)31.7742

Descriptive statistics

Standard deviation27.434764
Coefficient of variation (CV)1.4964004
Kurtosis0.34713909
Mean18.333838
Median Absolute Deviation (MAD)1.3727
Skewness1.3371083
Sum5417630.9
Variance752.66626
MonotonicityNot monotonic
2022-12-20T14:04:54.489430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.677 1058
 
0.4%
78.3034 1046
 
0.4%
79.7171 1045
 
0.4%
81.1822 1043
 
0.4%
80.5097 1033
 
0.3%
82.7015 1029
 
0.3%
82.004 1021
 
0.3%
79.0683 1001
 
0.3%
76.3332 955
 
0.3%
75.6194 944
 
0.3%
Other values (8223) 285324
96.6%
ValueCountFrequency (%)
0.0559 1
< 0.1%
0.0569 1
< 0.1%
0.0575 2
< 0.1%
0.058 2
< 0.1%
0.0584 1
< 0.1%
0.0585 2
< 0.1%
0.0587 2
< 0.1%
0.0589 1
< 0.1%
0.059 1
< 0.1%
0.0591 1
< 0.1%
ValueCountFrequency (%)
199.8105 1
 
< 0.1%
131.804 1
 
< 0.1%
117.8584 3
 
< 0.1%
116.4568 2
 
< 0.1%
114.818 4
 
< 0.1%
113.4868 12
 
< 0.1%
111.9292 9
 
< 0.1%
110.6632 14
 
< 0.1%
109.181 25
< 0.1%
107.9756 35
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8215
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.001356
Minimum0.0546
Maximum182.3433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:54.662993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0546
5-th percentile0.1122
Q10.6084
median4.6678
Q346.8361
95-th percentile82.2033
Maximum182.3433
Range182.2887
Interquartile range (IQR)46.2277

Descriptive statistics

Standard deviation29.109381
Coefficient of variation (CV)1.2655506
Kurtosis-0.41876727
Mean23.001356
Median Absolute Deviation (MAD)4.5539
Skewness1.0052466
Sum6796877.8
Variance847.35603
MonotonicityNot monotonic
2022-12-20T14:04:54.814273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.6501 1172
 
0.4%
81.51 1136
 
0.4%
83.7703 1135
 
0.4%
85.3974 1106
 
0.4%
83.0507 1096
 
0.4%
80.6932 1092
 
0.4%
87.0883 1065
 
0.4%
82.2033 1054
 
0.4%
86.3116 1035
 
0.4%
79.2369 993
 
0.3%
Other values (8205) 284615
96.3%
ValueCountFrequency (%)
0.0546 1
< 0.1%
0.0553 1
< 0.1%
0.0562 1
< 0.1%
0.0568 1
< 0.1%
0.057 1
< 0.1%
0.0572 2
< 0.1%
0.0574 1
< 0.1%
0.0578 2
< 0.1%
0.058 1
< 0.1%
0.0583 1
< 0.1%
ValueCountFrequency (%)
182.3433 1
 
< 0.1%
141.6613 2
 
< 0.1%
123.6951 1
 
< 0.1%
114.1263 2
 
< 0.1%
112.8031 14
 
< 0.1%
111.2549 14
 
< 0.1%
109.9965 38
< 0.1%
108.5233 47
< 0.1%
107.3251 66
< 0.1%
105.9214 76
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7602
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.326875
Minimum0.0395
Maximum97.6414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:54.982614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0395
5-th percentile0.102
Q12.1442
median21.788
Q334.2691
95-th percentile51.1162
Maximum97.6414
Range97.6019
Interquartile range (IQR)32.1249

Descriptive statistics

Standard deviation17.51981
Coefficient of variation (CV)0.8214898
Kurtosis-0.8335225
Mean21.326875
Median Absolute Deviation (MAD)15.4495
Skewness0.34301847
Sum6302070.1
Variance306.94374
MonotonicityNot monotonic
2022-12-20T14:04:55.136883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.8093 1005
 
0.3%
34.4447 1001
 
0.3%
35.5988 996
 
0.3%
34.7712 990
 
0.3%
34.5923 979
 
0.3%
33.64 977
 
0.3%
34.1241 969
 
0.3%
35.257 967
 
0.3%
36.6641 963
 
0.3%
35.7879 952
 
0.3%
Other values (7592) 285700
96.7%
ValueCountFrequency (%)
0.0395 1
 
< 0.1%
0.0399 1
 
< 0.1%
0.0409 1
 
< 0.1%
0.0411 2
< 0.1%
0.0415 1
 
< 0.1%
0.0417 2
< 0.1%
0.0418 3
< 0.1%
0.0419 1
 
< 0.1%
0.042 1
 
< 0.1%
0.0421 2
< 0.1%
ValueCountFrequency (%)
97.6414 1
 
< 0.1%
90.6217 1
 
< 0.1%
82.6836 1
 
< 0.1%
81.8678 3
 
< 0.1%
80.9098 2
 
< 0.1%
80.1281 4
 
< 0.1%
79.2097 13
 
< 0.1%
78.4601 18
< 0.1%
77.5789 27
< 0.1%
76.8594 34
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7861
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.614771
Minimum0.0484
Maximum129.7955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:55.411301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0484
5-th percentile0.1152
Q11.915
median34.2792
Q353.0277
95-th percentile80.3481
Maximum129.7955
Range129.7471
Interquartile range (IQR)51.1127

Descriptive statistics

Standard deviation27.727946
Coefficient of variation (CV)0.85016528
Kurtosis-1.0046931
Mean32.614771
Median Absolute Deviation (MAD)25.7084
Skewness0.32288971
Sum9637632.3
Variance768.83899
MonotonicityNot monotonic
2022-12-20T14:04:55.567117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.8849 1394
 
0.5%
49.7203 1362
 
0.5%
50.562 1351
 
0.5%
51.7661 1346
 
0.5%
49.9804 1341
 
0.5%
48.8555 1333
 
0.5%
49.4117 1327
 
0.4%
51.1571 1326
 
0.4%
48.3116 1323
 
0.4%
50.296 1320
 
0.4%
Other values (7851) 282076
95.5%
ValueCountFrequency (%)
0.0484 1
< 0.1%
0.0491 1
< 0.1%
0.0495 1
< 0.1%
0.0496 1
< 0.1%
0.0497 1
< 0.1%
0.05 2
< 0.1%
0.0502 2
< 0.1%
0.0506 1
< 0.1%
0.0507 2
< 0.1%
0.0508 1
< 0.1%
ValueCountFrequency (%)
129.7955 1
 
< 0.1%
127.7625 2
 
< 0.1%
124.1949 6
 
< 0.1%
122.6378 8
 
< 0.1%
120.8197 9
 
< 0.1%
119.345 14
< 0.1%
117.6218 24
< 0.1%
116.223 16
< 0.1%
114.5874 25
< 0.1%
113.2589 34
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7868
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.547401
Minimum0.0488
Maximum175.4801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:55.796355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0488
5-th percentile0.1243
Q11.6403
median24.5003
Q350.8369
95-th percentile79.9474
Maximum175.4801
Range175.4313
Interquartile range (IQR)49.1966

Descriptive statistics

Standard deviation27.627238
Coefficient of variation (CV)0.93501417
Kurtosis-0.91216978
Mean29.547401
Median Absolute Deviation (MAD)24.1932
Skewness0.52400809
Sum8731227.4
Variance763.2643
MonotonicityNot monotonic
2022-12-20T14:04:55.941353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.2798 1196
 
0.4%
50.4951 1193
 
0.4%
49.8802 1189
 
0.4%
52.1297 1188
 
0.4%
81.5165 1154
 
0.4%
50.2138 1147
 
0.4%
48.1206 1146
 
0.4%
48.4314 1140
 
0.4%
50.8369 1132
 
0.4%
79.9474 1132
 
0.4%
Other values (7858) 283882
96.1%
ValueCountFrequency (%)
0.0488 1
< 0.1%
0.0492 1
< 0.1%
0.0494 1
< 0.1%
0.0499 2
< 0.1%
0.0501 1
< 0.1%
0.0503 2
< 0.1%
0.0507 1
< 0.1%
0.0508 1
< 0.1%
0.0509 1
< 0.1%
0.051 1
< 0.1%
ValueCountFrequency (%)
175.4801 1
 
< 0.1%
145.1587 2
 
< 0.1%
116.8232 2
 
< 0.1%
115.3585 5
 
< 0.1%
113.6483 7
 
< 0.1%
112.2611 19
 
< 0.1%
110.6402 47
< 0.1%
109.3244 46
< 0.1%
107.7859 86
< 0.1%
106.5363 101
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7758
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.756806
Minimum0.0526
Maximum153.387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:56.112031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0526
5-th percentile0.1224
Q12.0116
median33.3892
Q353.7185
95-th percentile81.3453
Maximum153.387
Range153.3344
Interquartile range (IQR)51.7069

Descriptive statistics

Standard deviation28.056579
Coefficient of variation (CV)0.85651145
Kurtosis-1.0613708
Mean32.756806
Median Absolute Deviation (MAD)26.2092
Skewness0.32829863
Sum9679603.4
Variance787.17164
MonotonicityNot monotonic
2022-12-20T14:04:56.274967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.6787 1377
 
0.5%
52.4174 1374
 
0.5%
53.7185 1334
 
0.5%
49.9924 1333
 
0.5%
50.8483 1326
 
0.4%
49.1134 1324
 
0.4%
51.7898 1323
 
0.4%
52.7077 1321
 
0.4%
51.4536 1318
 
0.4%
54.0231 1312
 
0.4%
Other values (7748) 282157
95.5%
ValueCountFrequency (%)
0.0526 1
< 0.1%
0.0531 2
< 0.1%
0.0544 2
< 0.1%
0.0546 1
< 0.1%
0.0547 1
< 0.1%
0.0549 2
< 0.1%
0.055 1
< 0.1%
0.0551 1
< 0.1%
0.0553 1
< 0.1%
0.0556 1
< 0.1%
ValueCountFrequency (%)
153.387 1
 
< 0.1%
134.7263 1
 
< 0.1%
115.5214 2
 
< 0.1%
113.8957 9
 
< 0.1%
112.5752 18
 
< 0.1%
111.0301 26
 
< 0.1%
109.7743 50
< 0.1%
108.304 80
< 0.1%
107.1083 90
< 0.1%
105.7075 110
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6188
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.988712
Minimum0.033
Maximum108.2735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:56.449937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile0.0996
Q112.0932
median28.0407
Q342.1236
95-th percentile62.9816
Maximum108.2735
Range108.2405
Interquartile range (IQR)30.0304

Descriptive statistics

Standard deviation20.455817
Coefficient of variation (CV)0.73085954
Kurtosis-0.79654557
Mean27.988712
Median Absolute Deviation (MAD)14.3717
Skewness0.16927675
Sum8270636.3
Variance418.44044
MonotonicityNot monotonic
2022-12-20T14:04:56.604685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.6634 1041
 
0.4%
0.1003 1030
 
0.3%
39.8241 1017
 
0.3%
41.6828 1014
 
0.3%
54.073 1012
 
0.3%
0.1002 1007
 
0.3%
41.0575 1001
 
0.3%
40.4129 998
 
0.3%
40.6381 998
 
0.3%
0.1005 997
 
0.3%
Other values (6178) 285384
96.6%
ValueCountFrequency (%)
0.033 1
< 0.1%
0.0332 1
< 0.1%
0.0336 1
< 0.1%
0.0337 2
< 0.1%
0.0339 1
< 0.1%
0.0341 1
< 0.1%
0.0342 2
< 0.1%
0.0344 1
< 0.1%
0.0345 1
< 0.1%
0.0346 1
< 0.1%
ValueCountFrequency (%)
108.2735 1
 
< 0.1%
105.4803 1
 
< 0.1%
94.5965 2
 
< 0.1%
93.4149 2
 
< 0.1%
92.4523 3
 
< 0.1%
91.3229 6
< 0.1%
90.4024 1
 
< 0.1%
89.3217 13
< 0.1%
88.4405 10
< 0.1%
87.4055 11
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6165
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.490796
Minimum0.0292
Maximum87.7234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:56.764325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0292
5-th percentile0.0967
Q18.3959
median21.6329
Q334.4416
95-th percentile53.7929
Maximum87.7234
Range87.6942
Interquartile range (IQR)26.0457

Descriptive statistics

Standard deviation17.309692
Coefficient of variation (CV)0.76963445
Kurtosis-0.61933071
Mean22.490796
Median Absolute Deviation (MAD)12.9442
Skewness0.36296042
Sum6646007.8
Variance299.62543
MonotonicityNot monotonic
2022-12-20T14:04:56.912175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0973 3236
 
1.1%
0.0972 3160
 
1.1%
0.0971 3091
 
1.0%
0.0975 3015
 
1.0%
0.097 2955
 
1.0%
0.0968 2710
 
0.9%
0.0976 2678
 
0.9%
0.0967 2477
 
0.8%
0.0977 2052
 
0.7%
0.0966 2047
 
0.7%
Other values (6155) 268078
90.7%
ValueCountFrequency (%)
0.0292 1
 
< 0.1%
0.0293 2
 
< 0.1%
0.0297 2
 
< 0.1%
0.0298 1
 
< 0.1%
0.0299 3
< 0.1%
0.03 1
 
< 0.1%
0.0301 3
< 0.1%
0.0302 1
 
< 0.1%
0.0303 1
 
< 0.1%
0.0304 6
< 0.1%
ValueCountFrequency (%)
87.7234 1
 
< 0.1%
81.6883 1
 
< 0.1%
79.4435 2
 
< 0.1%
77.9697 3
 
< 0.1%
77.1883 9
< 0.1%
76.5489 13
< 0.1%
75.7953 16
< 0.1%
75.1784 18
< 0.1%
74.4511 22
< 0.1%
73.8555 20
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6406
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.128324
Minimum0.0368
Maximum102.3503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:57.171642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0368
5-th percentile0.118
Q17.751
median24.1369
Q340.4326
95-th percentile63.7159
Maximum102.3503
Range102.3135
Interquartile range (IQR)32.6816

Descriptive statistics

Standard deviation20.85417
Coefficient of variation (CV)0.7981442
Kurtosis-0.75561023
Mean26.128324
Median Absolute Deviation (MAD)16.2957
Skewness0.40902451
Sum7720893.7
Variance434.89642
MonotonicityNot monotonic
2022-12-20T14:04:57.325438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1199 2085
 
0.7%
0.1198 1984
 
0.7%
0.1201 1976
 
0.7%
0.1197 1974
 
0.7%
0.1195 1932
 
0.7%
0.1202 1887
 
0.6%
0.1194 1786
 
0.6%
0.1193 1669
 
0.6%
0.1204 1612
 
0.5%
0.1191 1572
 
0.5%
Other values (6396) 277022
93.7%
ValueCountFrequency (%)
0.0368 1
< 0.1%
0.0374 1
< 0.1%
0.0375 1
< 0.1%
0.0379 2
< 0.1%
0.0381 1
< 0.1%
0.0383 2
< 0.1%
0.0384 1
< 0.1%
0.0385 1
< 0.1%
0.0386 2
< 0.1%
0.0387 1
< 0.1%
ValueCountFrequency (%)
102.3503 1
 
< 0.1%
101.0719 1
 
< 0.1%
93.6563 1
 
< 0.1%
91.7066 1
 
< 0.1%
89.8357 4
 
< 0.1%
88.8467 8
< 0.1%
88.0388 9
< 0.1%
87.0883 17
< 0.1%
86.3116 13
< 0.1%
85.3974 13
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6286
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.099939
Minimum0.031
Maximum125.9891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:57.484607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.031
5-th percentile0.108
Q110.6346
median27.7396
Q342.5596
95-th percentile64.3359
Maximum125.9891
Range125.9581
Interquartile range (IQR)31.925

Descriptive statistics

Standard deviation20.934631
Coefficient of variation (CV)0.74500629
Kurtosis-0.83098379
Mean28.099939
Median Absolute Deviation (MAD)15.2444
Skewness0.21286203
Sum8303503.8
Variance438.25878
MonotonicityNot monotonic
2022-12-20T14:04:57.746234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1085 3563
 
1.2%
0.1086 3268
 
1.1%
0.1084 3080
 
1.0%
0.1082 2683
 
0.9%
0.1088 2610
 
0.9%
0.1081 2203
 
0.7%
0.1089 2194
 
0.7%
0.108 1942
 
0.7%
0.109 1905
 
0.6%
0.1092 1690
 
0.6%
Other values (6276) 270361
91.5%
ValueCountFrequency (%)
0.031 1
 
< 0.1%
0.0312 2
< 0.1%
0.0316 2
< 0.1%
0.0317 2
< 0.1%
0.0318 3
< 0.1%
0.0319 2
< 0.1%
0.032 2
< 0.1%
0.0321 4
< 0.1%
0.0323 2
< 0.1%
0.0324 2
< 0.1%
ValueCountFrequency (%)
125.9891 1
 
< 0.1%
93.9401 1
 
< 0.1%
90.9514 1
 
< 0.1%
90.1079 1
 
< 0.1%
89.1159 3
 
< 0.1%
88.3056 5
 
< 0.1%
87.3522 11
 
< 0.1%
86.5731 14
 
< 0.1%
85.6562 28
< 0.1%
84.9067 41
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6285
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.150859
Minimum0.033
Maximum98.3064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:57.916810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile0.1075
Q19.78625
median26.6439
Q339.8842
95-th percentile56.9212
Maximum98.3064
Range98.2734
Interquartile range (IQR)30.09795

Descriptive statistics

Standard deviation19.075451
Coefficient of variation (CV)0.72943879
Kurtosis-0.83286627
Mean26.150859
Median Absolute Deviation (MAD)14.203
Skewness0.131398
Sum7727552.7
Variance363.87284
MonotonicityNot monotonic
2022-12-20T14:04:58.072982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1087 1306
 
0.4%
0.1088 1305
 
0.4%
0.109 1297
 
0.4%
0.1091 1284
 
0.4%
0.1093 1234
 
0.4%
0.1086 1222
 
0.4%
0.1094 1186
 
0.4%
0.1095 1153
 
0.4%
0.1101 1140
 
0.4%
0.1084 1136
 
0.4%
Other values (6275) 283236
95.9%
ValueCountFrequency (%)
0.033 1
 
< 0.1%
0.0331 1
 
< 0.1%
0.0333 1
 
< 0.1%
0.0335 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.034 2
< 0.1%
0.0341 4
< 0.1%
0.0342 4
< 0.1%
0.0343 3
< 0.1%
0.0344 1
 
< 0.1%
ValueCountFrequency (%)
98.3064 1
 
< 0.1%
93.0504 1
 
< 0.1%
87.5281 1
 
< 0.1%
86.7475 2
 
< 0.1%
85.8287 1
 
< 0.1%
85.0777 6
 
< 0.1%
84.1934 11
 
< 0.1%
83.4702 20
< 0.1%
82.6184 28
< 0.1%
81.9217 35
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6357
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.923884
Minimum0.0334
Maximum78.1157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:58.234440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0334
5-th percentile0.1011
Q17.8681
median22.1146
Q335.1925
95-th percentile53.8044
Maximum78.1157
Range78.0823
Interquartile range (IQR)27.3244

Descriptive statistics

Standard deviation17.579924
Coefficient of variation (CV)0.76688243
Kurtosis-0.70117203
Mean22.923884
Median Absolute Deviation (MAD)13.2743
Skewness0.32625116
Sum6773984.7
Variance309.05372
MonotonicityNot monotonic
2022-12-20T14:04:58.392613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1031 1256
 
0.4%
0.103 1243
 
0.4%
0.1029 1233
 
0.4%
0.1027 1213
 
0.4%
0.1026 1207
 
0.4%
0.1032 1200
 
0.4%
0.1034 1183
 
0.4%
0.1035 1136
 
0.4%
0.1037 1129
 
0.4%
0.1025 1108
 
0.4%
Other values (6347) 283591
96.0%
ValueCountFrequency (%)
0.0334 1
< 0.1%
0.0335 1
< 0.1%
0.0339 1
< 0.1%
0.0341 2
< 0.1%
0.0342 1
< 0.1%
0.0343 1
< 0.1%
0.0344 2
< 0.1%
0.0346 1
< 0.1%
0.0351 1
< 0.1%
0.0352 1
< 0.1%
ValueCountFrequency (%)
78.1157 1
 
< 0.1%
76.7411 1
 
< 0.1%
76.0113 3
 
< 0.1%
75.4135 2
 
< 0.1%
74.7083 5
 
< 0.1%
74.1305 10
 
< 0.1%
73.4487 13
 
< 0.1%
72.8899 14
 
< 0.1%
72.2303 22
< 0.1%
71.6896 47
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6230
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.025789
Minimum0.0321
Maximum111.4358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:04:58.563687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0321
5-th percentile0.107
Q19.7012
median27.5851
Q345.0239
95-th percentile68.9791
Maximum111.4358
Range111.4037
Interquartile range (IQR)35.3227

Descriptive statistics

Standard deviation22.413463
Coefficient of variation (CV)0.77219135
Kurtosis-0.90802284
Mean29.025789
Median Absolute Deviation (MAD)17.4388
Skewness0.2928302
Sum8577091.6
Variance502.36333
MonotonicityNot monotonic
2022-12-20T14:04:58.714758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1075 2901
 
1.0%
0.1077 2797
 
0.9%
0.1074 2754
 
0.9%
0.1079 2729
 
0.9%
0.1078 2655
 
0.9%
0.108 2597
 
0.9%
0.1072 2376
 
0.8%
0.1071 2144
 
0.7%
0.1082 2037
 
0.7%
0.107 1900
 
0.6%
Other values (6220) 270609
91.6%
ValueCountFrequency (%)
0.0321 1
 
< 0.1%
0.0322 3
< 0.1%
0.0324 2
 
< 0.1%
0.0325 2
 
< 0.1%
0.0328 1
 
< 0.1%
0.0329 1
 
< 0.1%
0.033 3
< 0.1%
0.0332 2
 
< 0.1%
0.0333 5
< 0.1%
0.0334 2
 
< 0.1%
ValueCountFrequency (%)
111.4358 1
 
< 0.1%
101.1097 1
 
< 0.1%
94.5298 1
 
< 0.1%
93.4235 1
 
< 0.1%
86.9716 1
 
< 0.1%
86.0327 7
 
< 0.1%
85.2654 4
 
< 0.1%
84.3623 7
 
< 0.1%
83.6241 14
< 0.1%
82.7549 30
< 0.1%

Interactions

2022-12-20T14:04:41.110021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:28.549248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:33.853944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:37.345087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:41.065624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:44.779865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:48.417324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:52.182092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:55.822005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:59.473551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:03.275519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:06.777222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:10.506795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:14.264128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:18.687754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:22.553588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:26.207138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:29.837117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:33.610924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:37.249669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:41.280197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:28.751912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:34.009538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:37.504291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:41.231041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:44.958867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:48.589185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:52.345239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:56.071373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:59.643495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:03.438328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:07.041341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:10.687039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:14.443999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:18.871305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:22.720378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:26.376260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:30.012824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:33.778222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:37.424845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:41.462008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:28.945710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:34.188259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:37.668505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:41.425195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:45.149331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:48.779198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:52.526434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:56.245416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:59.847796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:03.619127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:07.235630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:10.879350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:14.676172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:19.152904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:22.902102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:26.564140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:30.197824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:33.969944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:37.723472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:41.635731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:29.103303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:34.367540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:37.868078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:41.601276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:45.305585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:48.951610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:52.695887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:56.424926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:00.037896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:03.778097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:07.411493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:11.054196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:14.888618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:19.319403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:23.102433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:26.754615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:30.376652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:34.148720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:37.896956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:41.818664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:29.288389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:34.560175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:38.189613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:41.796296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:45.494699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:49.133991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:52.873322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:56.604086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:00.231056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:03.965017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:07.609807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:11.240294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:15.077646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:19.587685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:23.282925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:26.945577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:30.563744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:34.338614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:38.089871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:42.004723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:29.487564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:34.730716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:38.370567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:41.982038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:45.664229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:49.336640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:53.049580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:56.782600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:00.417335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:04.139657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:07.784626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:11.417649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:15.261393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:19.752843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:23.481461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:27.119331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:30.749021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:34.517414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:38.272008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:42.190296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:29.665667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:34.906261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:38.596365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:42.163151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:45.849162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:49.551945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:53.216543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:56.964709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:00.614454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:04.317201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:07.972808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:11.612690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:15.466975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:19.958177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:23.669783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:27.298488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:30.939230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:34.708083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:38.468048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:42.370130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:29.856273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:35.088570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:38.776969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:42.344913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:46.021474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:49.813121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:53.395266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:57.137184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:00.806245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:04.488840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:08.149860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:11.794688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:15.645446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:20.133693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:23.841697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:27.481371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:31.120206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:34.887630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:38.684166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:42.553394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:30.015499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:35.270515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:38.963019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:42.542110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:03:51.823551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:55.269069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:59.104507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:02.901551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:06.425367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:10.149126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:13.893919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:18.243011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:22.200782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:25.852558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:29.464322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:33.251297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:36.888445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:40.735273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:44.627369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:33.691631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:37.140195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:40.897009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:44.602845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:48.250705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:52.014409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:55.542493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:03:59.288049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:03.101828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:06.607144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:10.337117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:14.086651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:18.487076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:22.383713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:26.039115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:29.659240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:33.435149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:37.076576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:04:40.931260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:04:58.875635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:04:59.143802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:04:59.409672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:04:59.686691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:05:00.067908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:04:44.883240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:04:45.921587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.048.6826.3244.71140.20002.32531.55055.876427.442941.593835.193342.345356.415844.538154.132557.145250.030648.662866.8445
10.3080.048.6826.3242.23720.20002.04931.41685.079926.335342.185834.454940.559552.344644.320757.728962.094750.346647.584664.8363
20.6160.048.6826.3241.78470.20001.82171.30874.445427.033243.027333.040339.437555.636343.140554.503360.760750.346645.784163.3641
30.9260.048.6826.3241.32940.19991.62961.21843.875325.327340.216630.998138.032555.988044.062555.196256.806849.206846.278265.2822
41.2340.048.6826.3240.89190.20001.48151.14063.437224.804139.844129.720737.366052.344642.493455.196260.760750.030646.278263.8761
51.5420.048.6826.3240.79360.19961.34861.07653.054820.650539.314228.048336.407953.035542.493452.814360.760749.461446.552264.3090
61.8510.048.6826.3240.69500.20001.24311.02202.752123.818138.766026.646134.757350.031544.062557.382656.076049.461446.052363.3641
72.1570.048.6826.3240.59740.20001.15280.97412.494023.270339.119024.653433.389253.035543.596754.816159.482048.904745.300161.5540
82.4670.048.6826.3240.66360.20001.07600.93132.265922.194335.848123.200331.640853.744542.493453.827354.668448.360246.052364.8363
92.7740.048.6826.3240.77170.20001.00990.89502.084521.848634.684421.892130.617951.014642.693656.635156.076049.461445.083462.4461
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29548990906.8890.061.8426.780.55700.19900.53280.56820.78515.45606.69243.65735.808041.682839.248249.520252.974443.306035.952953.8426
29549090907.1980.061.8426.780.03860.20000.52130.56070.76575.09106.10093.43545.360847.963238.511348.411248.263043.306036.402055.5538
29549190907.5060.061.8426.780.00000.20000.51020.55430.74804.81635.67183.23804.973144.247939.044645.840752.336842.876335.352853.1748
29549290907.8150.061.8426.780.00000.20000.50020.54770.73144.49425.20463.06864.626348.544537.960147.874847.493343.946336.402054.5271
29549390908.1230.061.8426.780.00000.19900.49090.54170.71674.25974.84822.88694.283645.250538.152947.587051.105642.041335.192553.1748
29549490908.4330.061.8426.780.00000.20000.48210.53560.70223.97544.46992.74933.985848.225737.801047.068346.978043.744235.352853.8426
29549590908.7430.061.8426.780.00000.19900.47450.53020.68843.77284.12922.60643.722144.743737.611645.358351.435742.228335.192553.1748
29549690909.0510.061.8426.780.00000.20000.46610.52560.67593.54843.86192.48263.469744.471939.627148.411249.670341.237634.209152.1742
29549790909.3600.061.8426.780.00000.19970.45930.52030.66433.34733.56772.36453.250547.963236.602645.358346.202842.645433.934453.8426
29549890909.6680.061.8426.780.00000.19960.45230.51550.65253.16763.33192.25553.057943.033136.933045.358350.245441.635734.616652.5231